# !user/bing/env python3
# -*- coding: utf-8 -*-

import tensorflow as tf
from numpy.random import RandomState

"""
    神经网络demo
"""

# 定义训练batch的大小
BATCH_SIZE = 8

# stddev 标准差 seed
# 定义神经网络的参数
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1))
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1))

x = tf.placeholder(tf.float32, shape=(None, 2), name='input-x')
y_ = tf.placeholder(tf.float32, shape=(None, 1), name='input-y')

# 定义神经网络前向传播的过程
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

# 定义损失函数和反向传播的算法
cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

# 通过随机数生成模拟数据集
rdm = RandomState(1)
DATA_SIZE = 128
X = rdm.rand(DATA_SIZE, 2)

# x1 + x2 < 1表示正样本(合格品)
Y = [[int(x1 + x2) < 1] for x1, x2 in X]

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print(sess.run(w1))
    print(sess.run(w2))

    STEPS = 5000
    for i in range(STEPS):
        start = (i * BATCH_SIZE) % DATA_SIZE
        end = min(start + BATCH_SIZE, DATA_SIZE)
        sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})

        if i % 100 == 0:
            total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})
            print("after %d training step(s), cross entropy on all data is %g" % (i, total_cross_entropy))

    print(sess.run(w1))
    print(sess.run(w2))
